AIE 721 – Advanced Reinforcement Learning

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About Course

  • This course goes beyond foundational reinforcement learning, covering advanced topics such as deep reinforcement learning, multi-agent systems, and hierarchical reinforcement learning.
  • The content is geared toward students who already have a solid grounding in AI and are looking to explore cutting-edge techniques and applications in fields like robotics and autonomous systems.
  • Students will engage in both theoretical study and practical projects to apply these advanced techniques to real-world scenarios.

What Will You Learn?

  • 1. Demonstrate proficiency in advanced reinforcement learning algorithms and their practical implementations.
  • 2. Apply deep reinforcement learning to solve real-world problems in robotics and autonomous systems.
  • 3. Design and implement multi-agent reinforcement learning systems and analyze their performance.
  • 4. Develop and apply hierarchical reinforcement learning frameworks to complex decision-making tasks.
  • 5. Critically evaluate recent research and contribute to the field through original projects and studies.

Course Content

Week 1: Introduction to Advanced Reinforcement Learning

  • Introduction to Advanced Reinforcement Learning
  • LO1: Explain the scope and importance of Advanced Reinforcement Learning techniques
  • LO2: Describe key differences between basic and Advanced Reinforcement Learning approaches
  • LO3: Summarize course objectives, structure, and expected learning outcomes
  • Multiple Choice Questions
  • True/False Questions
  • Scenario-Based Multiple-Choice Questions
  • Key Terms and Concepts Questions
  • Short Answer Questions
  • Written Assignment
  • Presentation Task
  • Role-Playing Activity
  • Peer Review Task
  • Exercises and Activities Adaptation

Week 2: Deep Reinforcement Learning I
Deep Reinforcement Learning I

Week 3: Deep Reinforcement Learning II
Deep Reinforcement Learning II

Week 4: Multi-Agent Systems I
Multi-Agent Systems I

Week 5: Multi-Agent Systems II
Multi-Agent Systems II

Week 6: Hierarchical Reinforcement Learning I
Hierarchical Reinforcement Learning I

Week 7: Hierarchical Reinforcement Learning II
Hierarchical Reinforcement Learning II

Week 8: Midterm Test / Assignment

Week 9: Applications in Robotics
Applications in Robotics

Week 10: Applications in Autonomous Systems
Applications in Autonomous Systems

Week 11: Safety and Ethics in Reinforcement Learning
Safety and Ethics in Reinforcement Learning

Week 12: Performance Optimization in RL Systems
Performance Optimization in RL Systems

Week 13: Future Trends in Reinforcement Learning
Future Trends in Reinforcement Learning

Week 14: Research in Advanced Reinforcement Learning
Research in Advanced Reinforcement Learning

Week 15: Course Review
Course Review

Week 16: Final Test / Project

Florida Coastal University

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